Sökning: "object detection"

Visar resultat 6 - 10 av 621 uppsatser innehållade orden object detection.

1. 6. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Xinchen Wang; [2024]
   Nyckelord :Deep Learning; Semantic segmentation; Network optimization; Network pruning; Torch Pruning; YOLOv8; Network Depth; Djup lärning; Semantisk segmentering; Nätverksoptimering; Nätverksbeskärning; Fackelbeskärning; YOLOv8; Nätverksdjup;

   Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER

3. 7. IDENTIFICATION OF ENVIRONMENTALLY RELEVANT BENTHIC FORAMINIFERA FROM THE SKAGERRAK FJORDS BY DEEP LEARNING IMAGE MODELING

   Master-uppsats, Göteborgs universitet / Institutionen för biologi och miljövetenskap

   Författare :Marko Plavetic; [2023-06-26]
   Nyckelord :benthic foraminifera; deep learning; environmental monitoring; YOLOv7;

   Sammanfattning : Over the several past decades, there has been increasing interest in using foraminifera as environmental indicators for coastal marine environments. As compared to macrofauna, which are currently used in environmental studies, foraminifera offer several distinct advantages as bioindicators, including short generation times, a high number of individuals per small sample volume, hard and durable tests with high preservation potential, and low cost of sample extraction. LÄS MER

4. 8. Methods for Developing TinyConvolutional Neural Networksfor Deployment on EmbeddedSystems

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Egemen Yiğit Kömürcü; [2023]
   Nyckelord :;

   Sammanfattning : With the recent development in the Deep Learning area, computationally heavy tasks like object detection in images have become easier to compute and take less time to execute with powerful GPUs. Also, when employing sufficiently larger models, these daily tasks are predicted with greater accuracy. LÄS MER

5. 9. Real time Optical  Character Recognition  in  steel  bars  using YOLOV5

   Master-uppsats, Blekinge Tekniska Högskola

   Författare :Monica Gattupalli; [2023]
   Nyckelord :Deep learning; Object detection; Tesseract OCR; YOLOV5; YOLOV5- obb;

   Sammanfattning : Background.Identifying the quality of the products in the manufacturing industry is a challenging task. Manufacturers use needles to print unique numbers on the products to differentiate between good and bad quality products. However, identi- fying these needle printed characters can be difficult. LÄS MER

7. 10. Instance segmentation using 2.5D data

   Master-uppsats, Linköpings universitet/Institutionen för systemteknik

   Författare :Jonathan Öhrling; [2023]
   Nyckelord :instance segmentation; multi-modality; segmentation; multi-modality fusion; CNN; RGBD; ToF; Mask R-CNN; RTMDet; MMDetection; COCO; NYUDepth;

   Sammanfattning : Multi-modality fusion is an area of research that has shown promising results in the domain of 2D and 3D object detection. However, multi-modality fusion methods have largely not been utilized in the domain of instance segmentation. LÄS MER